A method of identifying an epileptogenic zone of a subjects brain includes: receiving a plurality N of physiological brain signals that extend over a duration, each of the plurality N of physiological brain signals acquired from the subjects brain; calculating within a time window a state transition matrix based on at least a portion of each of the plurality N of physiological brain signals, wherein the state transition matrix is a linear time invariant model of a network of N nodes corresponding to the plurality N of physiological brain signals; calculating a minimum norm of a perturbation on the state transition matrix that causes the network to transition from a stable state to an unstable state; and assigning a fragility metric to each of the plurality N of physiological brain signals based on the minimum norm of the perturbation for that physiological brain signal.

Embodiment relate to a system for developing a model to classify continuous glucose monitoring (CGM) data. The system includes a processor and computer memory having instructions stored thereon that when executed will cause the processor to determine whether two CGM profiles match based on a similarity of shapes of the two CGM profiles, each CGM profile including a data set of CGM measurements. The processor designates two matching CGM profiles as a CGM profile pair. The processor transforms the CGM profile pair into a motif. The processor labels the motif as a labelled motif based on a clinical characteristic. The processor recursively repeats the determine, designate and transform steps of a CGM profile pairing process until a finite set of motifs is created, which includes the labelled motif as a classified data point. The processor monitor, analyzes, or influences a concentration of glucose levels in a fluid.

A stimulation system includes an energy source, an electronics unit with a controller, and an actuator that is coupled with the electronics unit and/or the energy source. The actuator emits electromagnetic waves for stimulation of genetically manipulated tissue. The electronics unit is disposed in a housing. The stimulation system is configured for at least temporary implantation in a human or animal body. The controller controls the stimulation of tissue in the body by way of the electromagnetic waves emitted by the actuator. A selector of the stimulation system selects the area of the said tissue for stimulation. The selector includes a masking device for masking certain areas of the tissue, so that an intensity of the stimulation for the masked areas is reduced or equal to zero.

A method for contactless vital sign monitoring includes transmitting, via a transceiver, radar signals for object detection. The method also includes generating a clutter removed channel impulse response from received reflections of the radar signals a portion of which are reflected off of a living object. The method further includes identifying a set of range bins corresponding to a position of the living object. Additionally, the method includes identifying a first set of signal components representing a respiration rate of the living object and a second set of signal components representing a heart rate of the living object.

The present invention relates to a method and system for calculating eating bites of a user. The method comprises: (a) continuously measuring the electrical properties data of mastication of a user for a predetermined period of time; (b) periodically determining single eating bites according to the data obtained in step (a) through a time interval; (c) periodically storing the bites determined throughout the predetermined period of time, through a time interval.

A method and system for heterogeneous event detection. Sensor data is obtained and divided into discrete data windows. Each data window is defined by and corresponds to a time period of the sensor data. A time-frequency representation over the time period is calculated for each data window. A filter mask is calculated based on the data window corresponding to the time-frequency representation. The filter mask is applied for reverting the time-frequency representation to a time representation, resulting in filtered data. Features, such as extrema or other inflection points, are identified in the filtered data. The features define events, and transforming the time-frequency representation back into the time domain emphasizes differences between more and less prominent frequencies, facilitating identification of heterogeneous events. The method and system may be applied to body movements of people or animals, automaton movement, audio signals, light intensity, or any suitable time-dependent variable.

Apparatus and method are provided for synchronized multiple vital health measurements. In one novel aspect, an integrated wearable device with multiple sensors that can collect multiple vital health signals, digitize them, send them through wireless network to a receiver. In one embodiment, the wearable device has a plurality of different types of sensors including at least one or more acoustic-to-electric sensors collecting phonocardiogram (PCG) electrical signal and one or more electrocardiogram (ECG) sensors, a control module includes a synchronization circuitry that synchronizes measurements of the plurality of different types of sensors. In another novel aspect, a system performs a synchronized measurement using a plurality type of health-monitoring sensors, performs a correlation analysis of the plurality of measurement results using selected one or more analytical rules, and obtains a set of parameters with recognized medical values and generating one or more medical health records based on the correlation analysis.

Apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing electrodes or other sensors placed proximate to portions of the body below the head to develop raw signals without physically touching the body and penetrating hair and clothing. Additionally, apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing non-contacting sensors placed proximate to portions of the head to develop raw signals. The raw signals are filtered to produce analysis signals including frequency components relevant to brain electrical activity while attenuating unrelated frequency components. The apparatus and methods can be used for biofeedback-based attention training, human performance training, gaming, biometrics, cognitive state detection, and relaxation training. Either wired or wireless signal connections are made to electronic circuitry, typically including a digital computer, for performing signal processing and analysis functions.

A motion data processing method and a motion monitoring system provided in the present disclosure may process an electromyography (EMG) signal in the frequency domain or time domain to identify an abnormal signal in the EMG signal, such as an abrupt signal, a missing signal, a saturation signal, an oscillation signal, etc. caused by a high-pass filtering algorithm. The motion data processing method and the motion monitoring system may further perform a data sampling operation on the EMG signal through a data sampling algorithm, and predict data corresponding to the time point when the abnormal signal appears based on the sampling data, so as to obtain prediction data, and replace the abnormal signal by using the prediction data to correct the abnormal signal. The motion data processing method and the motion monitoring system may not merely accurately identify the abnormal signal, but further correct the abnormal signal, so that the corrected data may be more in line with an actual motion of a user, thereby improving user experience.

The invention relates to a method for detecting epileptic or psychogenic seizures, comprising the steps of: a. recording a large number of temporally sequential parameter values for a parameter type, wherein a parameter value is determined on the basis of a series of temporally sequential RR intervals, the series of temporally sequential parameter values preferably differing in that the series that served as the basis for determining the following parameter value includes, in place of the oldest RR interval of the series that served as the basis for determining the preceding parameter value (preceding series), the RR interval temporally subsequent to the most recent RR interval of the preceding series, b. comparing the time course of the parameter values with the time course of parameter values for the same parameter type that had been determined according to method step a, and the determination thereof is based on RR intervals that indicate a seizure (parameter reference values), c. identifying a seizure when the time course of the parameter values exhibits a characteristic of the time course of the parameter reference values (course characteristic) indicating a seizure.